Artificial intelligence classification model for macular degeneration images: a robust optimization framework for residual neural networks

Abstract Background The prevalence of chronic disease is growing in aging societies, and artificial-intelligence–assisted interpretation of macular degeneration images is a topic that merits research. This study proposes a residual neural network (ResNet) model constructed using uniform design. The...

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Autores principales:	Wen-Hsien Ho, Tian-Hsiang Huang, Po-Yuan Yang, Jyh-Horng Chou, Hong-Siang Huang, Li-Chung Chi, Fu-I Chou, Jinn-Tsong Tsai
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Residual Neural Network Uniform experimental design Hyperparameter optimization Macular degeneration classification Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/2824bc515e364981b4db36e5370d1485
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spelling	oai:doaj.org-article:2824bc515e364981b4db36e5370d14852021-11-14T12:12:56ZArtificial intelligence classification model for macular degeneration images: a robust optimization framework for residual neural networks10.1186/s12859-021-04085-91471-2105https://doaj.org/article/2824bc515e364981b4db36e5370d14852021-11-01T00:00:00Zhttps://doi.org/10.1186/s12859-021-04085-9https://doaj.org/toc/1471-2105Abstract Background The prevalence of chronic disease is growing in aging societies, and artificial-intelligence–assisted interpretation of macular degeneration images is a topic that merits research. This study proposes a residual neural network (ResNet) model constructed using uniform design. The ResNet model is an artificial intelligence model that classifies macular degeneration images and can assist medical professionals in related tests and classification tasks, enhance confidence in making diagnoses, and reassure patients. However, the various hyperparameters in a ResNet lead to the problem of hyperparameter optimization in the model. This study employed uniform design—a systematic, scientific experimental design—to optimize the hyperparameters of the ResNet and establish a ResNet with optimal robustness. Results An open dataset of macular degeneration images ( https://data.mendeley.com/datasets/rscbjbr9sj/3 ) was divided into training, validation, and test datasets. According to accuracy, false negative rate, and signal-to-noise ratio, this study used uniform design to determine the optimal combination of ResNet hyperparameters. The ResNet model was tested and the results compared with results obtained in a previous study using the same dataset. The ResNet model achieved higher optimal accuracy (0.9907), higher mean accuracy (0.9848), and a lower mean false negative rate (0.015) than did the model previously reported. The optimal ResNet hyperparameter combination identified using the uniform design method exhibited excellent performance. Conclusion The high stability of the ResNet model established using uniform design is attributable to the study’s strict focus on achieving both high accuracy and low standard deviation. This study optimized the hyperparameters of the ResNet model by using uniform design because the design features uniform distribution of experimental points and facilitates effective determination of the representative parameter combination, reducing the time required for parameter design and fulfilling the requirements of a systematic parameter design process.Wen-Hsien HoTian-Hsiang HuangPo-Yuan YangJyh-Horng ChouHong-Siang HuangLi-Chung ChiFu-I ChouJinn-Tsong TsaiBMCarticleResidual Neural NetworkUniform experimental designHyperparameter optimizationMacular degeneration classificationComputer applications to medicine. Medical informaticsR858-859.7Biology (General)QH301-705.5ENBMC Bioinformatics, Vol 22, Iss S5, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Residual Neural Network Uniform experimental design Hyperparameter optimization Macular degeneration classification Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
spellingShingle	Residual Neural Network Uniform experimental design Hyperparameter optimization Macular degeneration classification Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5 Wen-Hsien Ho Tian-Hsiang Huang Po-Yuan Yang Jyh-Horng Chou Hong-Siang Huang Li-Chung Chi Fu-I Chou Jinn-Tsong Tsai Artificial intelligence classification model for macular degeneration images: a robust optimization framework for residual neural networks
description	Abstract Background The prevalence of chronic disease is growing in aging societies, and artificial-intelligence–assisted interpretation of macular degeneration images is a topic that merits research. This study proposes a residual neural network (ResNet) model constructed using uniform design. The ResNet model is an artificial intelligence model that classifies macular degeneration images and can assist medical professionals in related tests and classification tasks, enhance confidence in making diagnoses, and reassure patients. However, the various hyperparameters in a ResNet lead to the problem of hyperparameter optimization in the model. This study employed uniform design—a systematic, scientific experimental design—to optimize the hyperparameters of the ResNet and establish a ResNet with optimal robustness. Results An open dataset of macular degeneration images ( https://data.mendeley.com/datasets/rscbjbr9sj/3 ) was divided into training, validation, and test datasets. According to accuracy, false negative rate, and signal-to-noise ratio, this study used uniform design to determine the optimal combination of ResNet hyperparameters. The ResNet model was tested and the results compared with results obtained in a previous study using the same dataset. The ResNet model achieved higher optimal accuracy (0.9907), higher mean accuracy (0.9848), and a lower mean false negative rate (0.015) than did the model previously reported. The optimal ResNet hyperparameter combination identified using the uniform design method exhibited excellent performance. Conclusion The high stability of the ResNet model established using uniform design is attributable to the study’s strict focus on achieving both high accuracy and low standard deviation. This study optimized the hyperparameters of the ResNet model by using uniform design because the design features uniform distribution of experimental points and facilitates effective determination of the representative parameter combination, reducing the time required for parameter design and fulfilling the requirements of a systematic parameter design process.
format	article
author	Wen-Hsien Ho Tian-Hsiang Huang Po-Yuan Yang Jyh-Horng Chou Hong-Siang Huang Li-Chung Chi Fu-I Chou Jinn-Tsong Tsai
author_facet	Wen-Hsien Ho Tian-Hsiang Huang Po-Yuan Yang Jyh-Horng Chou Hong-Siang Huang Li-Chung Chi Fu-I Chou Jinn-Tsong Tsai
author_sort	Wen-Hsien Ho
title	Artificial intelligence classification model for macular degeneration images: a robust optimization framework for residual neural networks
title_short	Artificial intelligence classification model for macular degeneration images: a robust optimization framework for residual neural networks
title_full	Artificial intelligence classification model for macular degeneration images: a robust optimization framework for residual neural networks
title_fullStr	Artificial intelligence classification model for macular degeneration images: a robust optimization framework for residual neural networks
title_full_unstemmed	Artificial intelligence classification model for macular degeneration images: a robust optimization framework for residual neural networks
title_sort	artificial intelligence classification model for macular degeneration images: a robust optimization framework for residual neural networks
publisher	BMC
publishDate	2021
url	https://doaj.org/article/2824bc515e364981b4db36e5370d1485
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Artificial intelligence classification model for macular degeneration images: a robust optimization framework for residual neural networks

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